Systems biology of lactic acid bacteria: a critical review

Understanding the properties of a system as emerging from the interaction of well described parts is the most important goal of Systems Biology. Although in the practice of Lactic Acid Bacteria (LAB) physiology we most often think of the parts as the proteins and metabolites, a wider interpretation of what a part is can be useful. For example, different strains or species can be the parts of a community, or we could study only the chemical reactions as the parts of metabolism (and forgetting about the enzymes that catalyze them), as is done in flux balance analysis. As long as we have some understanding of the properties of these parts, we can investigate whether their interaction leads to novel or unanticipated behaviour of the system that they constitute

Comparative Genome Analysis of Lactococcus lactis Indicates Niche Adaptation and Resolves Genotype/Phenotype Disparity

Lactococcus lactis is one of the most important micro-organisms in the dairy industry for the fermentation of cheese and buttermilk. Besides the conversion of lactose to lactate it is responsible for product properties such as flavor and texture, which are determined by volatile metabolites, proteolytic activity and exopolysaccharide production. While the species Lactococcus lactis consists of the two subspecies lactis and cremoris their taxonomic position is confused by a group of strains that, despite of a cremoris genotype, display a lactis phenotype. Here we compared and analyzed the (draft) genomes of 43 L. lactis strains, of which 19 are of dairy and 24 are of non-dairy origin. Machine-learning algorithms facilitated the identification of orthologous groups of protein sequences (OGs) that are predictors for either the taxonomic position or the source of isolation. This allowed the unambiguous categorization of the genotype/phenotype disparity of ssp. lactis and ssp. cremoris strains. A detailed analysis of phenotypic properties including plasmid-encoded genes indicates evolutionary changes during niche adaptations. The results are consistent with the hypothesis that dairy isolates evolved from plant isolates. The analysis further suggests that genomes of cremoris phenotype strains are so eroded that they are restricted to a dairy environment. Overall the genome comparison of a diverse set of strains allowed the identification of niche and subspecies specific genes. This explains evolutionary relationships and will aid the identification and selection of industrial starter cultures

Ampicillin-treated Lactococcus lactis MG1363 populations contain persisters as well as viable but non-culturable cells

Lactococcus lactis is used as cell-factory and strain selections are regularly performed to improve production processes. When selection regimes only allow desired phenotypes to survive, for instance by using antibiotics to select for cells that do not grow in a specific condition, the presence of more resistant subpopulations with a wildtype genotype severely slows down the procedure. While the food grade organism L. lactis is not often exposed to antibiotics we characterized its response to ampicillin in more detail, to better understand emerging population heterogeneity and how this might affect strain selection procedures. Using growth-dependent viability assays we identified persister subpopulations in stationary and exponential phase. Growth-independent viability assays revealed a 100 times larger subpopulation that did not grow on plates or in liquid medium, but had an intact membrane and could maintain a pH gradient. Over one third of these cells restored their intracellular pH when we induced a temporary collapse, indicating that this subpopulation was metabolically active and in a viable but non-culturable state. Exposure of L. lactis MG1363 to ampicillin therefore results in a heterogeneous population response with different dormancy states. These dormant cells should be considered in survival-based strain selection procedures.</p

Transcriptional response of Lactococcus lactis during bacterial emulsification

Microbial surface properties are important for interactions with the environment in which cells reside. Surface properties of lactic acid bacteria significantly vary and some strains can form strong emulsions when mixed with a hydrocarbon. Lactococcus lactis NCDO712 forms oil-in-water emulsions upon mixing of a cell suspension with petroleum. In the emulsion the bacteria locate at the oil-water interphase which is consistent with Pickering stabilization. Cells of strain NCDO712 mixed with sunflower seed oil did not stabilize the oil droplets. This study shows that the addition of either ethanol or ammonium sulfate led to cell aggregation, which subsequently allowed stabilizing oil-in-water emulsions. From this, we conclude that bacterial cell aggregation is important for emulsion droplet stabilization. To determine how bacterial emulsification influences the microbial transcriptome RNAseq analysis was performed on lactococci taken from the oil-water interphase. In comparison to cells in suspension 72 genes were significantly differentially expressed with a more than 4-fold difference. The majority of these genes encode proteins involved in transport processes and the metabolism of amino acids, carbohydrates and ions. Especially the proportion of genes belonging to the CodY regulon was high. Our results also point out that in a complex environment such as food fermentations a heterogeneous response of microbes might be caused by microbe-matrix interactions. In addition, microdroplet technologies are increasingly used in research. The understanding of interactions between bacterial cells and oil-water interphases is of importance for conducting and interpreting such experiments

Microdroplet screening and selection for improved microbial production of extracellular compounds

Microorganisms produce extracellular compounds that affect the final product quality in fermentation processes. Selection of overproducing mutants requires coupling of the extracellular product to the producer genotype, which can be achieved by single-cell compartmentalization. Emulsions contain up to billions of microdroplets/mL which significantly increases the screening throughput compared to microtiter-plate-based selections. Factors affecting the success of screening in microdroplets include the nature of the producing organism (robust, no invasive growth), the product (not soluble in oil) and the product assay (preferably fluorescence based). Together these factors determine the required microdroplet production technique and sorting set-up. Because microdroplets allow relatively inexpensive ultrahigh-throughput screening, they are likely to become a standard tool in the strain selection toolbox of the fermentation industry.(VLID)460219

Enhancement of amino acid production and secretion by Lactococcus lactis using a droplet-based biosensing and selection system

Amino acids are attractive metabolites for the pharmaceutical and food industry field. On one hand, the construction of microbial cell factories for large-scale production aims to satisfy the demand for amino acids as bulk biochemical. On the other hand, amino acids enhance flavor formation in fermented foods. Concerning the latter, flavor formation in dairy products, such as cheese is associated with the presence of lactic acid bacteria (LAB). In particular, Lactococcus lactis, one of the most important LAB, is used as a starter culture in fermented foods. The proteolytic activity of some L. lactis strains results in peptides and amino acids, which are flavor compounds or flavor precursors. However, it is still a challenge to isolate bacterial cells with enhanced amino acid production and secretion activity. In this work, we developed a growth-based sensor strain to detect the essential amino acids isoleucine, leucine, valine, histidine and methionine. Amino acids are metabolites that can be secreted by some bacteria. Therefore, our biosensor allowed us to identify wild-type L. lactis strains that naturally secrete amino acids, by using co-cultures of the biosensor strain with potential amino acid producing strains. Subsequently, we used this biosensor in combination with a droplet-based screening approach, and isolated three mutated L. lactis IPLA838 strains with 5-10 fold increased amino acid-secretion compared to the wild type. Genome re-sequencing revealed mutations in genes encoding proteins that participate in peptide uptake and peptide degradation. We argue that an unbalance in the regulation of amino acid levels as a result of these gene mutations may drive the accumulation and secretion of these amino acids. This biosensing system tackles the problem of selection for overproduction of secreted molecules, which requires the coupling of the product to the producing cell in the droplets

High local substrate availability stabilizes a cooperative trait

Cooperative behavior is widely spread in microbial populations. An example is the expression of an extracellular protease by the lactic acid bacterium Lactococcus lactis, which degrades milk proteins into free utilizable peptides that are essential to allow growth to high cell densities in milk. Cheating, protease-negative strains can invade the population and drive the protease-positive strain to extinction. By using multiple experimental approaches, as well as modeling population dynamics, we demonstrate that the persistence of the proteolytic trait is determined by the fraction of the generated peptides that can be captured by the cell before diffusing away from it. The mechanism described is likely to be relevant for the evolutionary stability of many extracellular substrate-degrading enzymes

Cell Surface Properties of Lactococcus lactis Reveal Milk Protein Binding Specifically Evolved in Dairy Isolates

Surface properties of bacteria are determined by the molecular composition of the cell wall and they are important for interactions of cells with their environment. Well-known examples of bacterial interactions with surfaces are biofilm formation and the fermentation of solid materials like food and feed. Lactococcus lactis is broadly used for the fermentation of cheese and buttermilk and it is primarily isolated from either plant material or the dairy environment. In this study, we characterized surface hydrophobicity, charge, emulsification properties, and the attachment to milk proteins of 55 L. lactis strains in stationary and exponential growth phases. The attachment to milk protein was assessed through a newly developed flow cytometry-based protocol. Besides finding a high degree of biodiversity, phenotype-genotype matching allowed the identification of candidate genes involved in the modification of the cell surface. Overexpression and gene deletion analysis allowed to verify the predictions for three identified proteins that altered surface hydrophobicity and attachment of milk proteins. The data also showed that lactococci isolated from a dairy environment bind higher amounts of milk proteins when compared to plant isolates. It remains to be determined whether the alteration of surface properties also has potential to alter starter culture functionalities

Neural processing of criticism and positive comments from relatives in individuals with schizotypal personality traits

Objectives. High negative expressed emotion by family members towards schizophrenia patients increases the risk of subsequent relapse. The study aimed to determine whether individuals with high schizotypy (HS) and low schizotypy (LS) would differ in activation of brain areas involved in cognitive control when listening to relative criticism

Crowdsourced mapping of unexplored target space of kinase inhibitors

Despite decades of intensive search for compounds that modulate the activity of particular protein targets, a large proportion of the human kinome remains as yet undrugged. Effective approaches are therefore required to map the massive space of unexplored compound–kinase interactions for novel and potent activities. Here, we carry out a crowdsourced benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data. We find the top-performing predictions are based on various models, including kernel learning, gradient boosting and deep learning, and their ensemble leads to a predictive accuracy exceeding that of single-dose kinase activity assays. We design experiments based on the model predictions and identify unexpected activities even for under-studied kinases, thereby accelerating experimental mapping efforts. The open-source prediction algorithms together with the bioactivities between 95 compounds and 295 kinases provide a resource for benchmarking prediction algorithms and for extending the druggable kinome